Mapping molecular data to Darwin Core

Which information is entered where?

Elizabeth Lawrence, Emilie Boulanger, Saara Suominen

2026-07-22

Identifying key information for Metabarcoding & qPCR pipelines

eDNA Metabarcoding: Key Information

Category 1 — DNA-derived occurrences — basisOfRecord = MaterialSample

From field collection to taxonomy — what to capture at each step

Step 1:⛵ Field Sampling

Field Sampling	Maps to
Coordinates, location names	Event table: decimalLatitude, decimalLongitude, locality
Date, time	Event table: eventDate, eventTime
Environmental measurements (temperature, salinity, depth…)	eMoF table: measurementType, measurementValue, measurementUnit
Sampling method / gear	Event table: samplingProtocol and/or eMoF table
Sample volume, filter size	DNA table: samp_size , size_frac

This is where the Event core table gets populated. Environmental measurements that aren’t DNA-specific (e.g. temperature, salinity) go in eMoF rather than the DNA extension — worth a quick reminder here since people sometimes want to cram everything into one table.

eDNA Metabarcoding: Key Information

Category 1 — DNA-derived occurrences — basisOfRecord = MaterialSample

From field collection to taxonomy — what to capture at each step

Step 2: 🧬 DNA Extraction

DNA Extraction	Maps to
Volume of sample processed	DNA table: samp_vol_we_dna_ext
Extraction method / kit	DNA table: nucl_acid_ext, nucl_acid_amp
Concentration	DNA table: concentration, concentrationUnit
Purity ratio (A260/280)	DNA table: ratioOfAbsorbance260_280

Extraction method matters a lot for reproducibility and comparing datasets — different kits can bias which taxa get amplified downstream, so it’s worth capturing even though it’s easy to skip. Purity ratio (260/280) is a quick QC signal for contamination that’s worth flagging even if participants don’t record it religiously yet.

eDNA Metabarcoding: Key Information

Category 1 — DNA-derived occurrences — basisOfRecord = MaterialSample

From field collection to taxonomy — what to capture at each step

Step 3: 🧪 DNA Amplification

DNA Amplification	Maps to (DNA table)
Target gene (e.g. COI, 18S rRNA)	DNA table: target_gene
Primer sequence + primer reference	DNA table: pcr_primer_forward, pcr_primer_name_forward, pcr_primer_reverse, pcr_primer_name_reverse + pcr_primer_reference
PCR conditions	DNA table: pcr_cond
Length of amplified product	DNA table: ampliconSize

Primer choice determines which taxa can even be detected — always cite the primer reference, don’t just paste the sequence, so users can trace back to the original validation. PCR conditions (annealing temp, cycle number) belong here too even though they feel like lab-notebook detail; they matter for anyone trying to reproduce or compare your results.

eDNA Metabarcoding: Key Information

Category 1 — DNA-derived occurrences — basisOfRecord = MaterialSample

From field collection to taxonomy — what to capture at each step

Step 4: 💻 Sequences

Sequences	Maps to
Sequencing platform + library layout	DNA table: seq_meth + lib_layout
Sequences per sample	DNA table: DNA_sequence (linked by occurrenceID)
Public repository where sequences deposited (NCBI, ENA)	Occurrence table: associatedSequences

This is the last step before formatting — everything captured across the last 3 slides plus this one feeds into either the Occurrence table or the DNA extension. Reference database + pipeline (e.g. “DADA2 v1.18” + “SILVA 138”) matter a lot for reproducibility since taxonomy assignments can shift as databases update — this is what otu_class_appr and otu_db are for.

eDNA Metabarcoding: Key Information

Category 1 — DNA-derived occurrences — basisOfRecord = MaterialSample

From field collection to taxonomy — what to capture at each step

Step 5: 🐠 Taxonomy

Taxonomy	Maps to
Taxonomy assignment + annotation confidence	Occurrence table: scientificName, taxonID DNA table: annot
Reference database + pipeline used	Occurrence table: identificationRemarks, identificationReferences DNA table: otu_db + sop
Number of sequence reads per taxon & total	Occurence table: organismQuantity, organismQuantityType, & sampleSizeValue, sampleSizeUnit

This is the last step before formatting — everything captured across the last 3 slides plus this one feeds into either the Occurrence table or the DNA extension. Reference database + pipeline (e.g. “DADA2 v1.18” + “SILVA 138”) matter a lot for reproducibility since taxonomy assignments can shift as databases update — this is what otu_class_appr and otu_db are for.

Key Metadata: Quantitative PCR (qPCR)

Category 3 — Targeted species detection — basisOfRecord = MaterialSample

Targeted species detection — additional fields vs metabarcoding

⛵ Step 1: Field Sampling → same as metabarcoding

🧬 Step 2: DNA Extraction → same as metabarcoding

🧪 Step 3: DNA Amplification → PCR conditions → pcr_cond

🎯 Step 4/5: Target & Quantity

Target & Quantity	Maps to
Target gene region	target_gene, target_subfragment
Primers	pcr_primers
Copy number + concentration	estimatedNumberOfCopies + concentration, concentrationUnit
LOD, Cq, baseline	pcr_primer_lod, quantificationCycle, baselineValue

qPCR = Category 3 — targeted species detection. Adds qPCR-specific fields: probeReporter, probeQuencher, estimatedNumberOfCopies, thresholdQuantificationCycle, and baselineValue. Key difference from metabarcoding: you are detecting one specific taxon, so scientificName and scientificNameID are required from the start. Use basisOfRecord = MaterialSample.

Special cases

Unknown sequences

• Many sequences in any given study will remain uncharacterized
• Populate the scientificName field with Biota incertae sedis
• Or the lowest taxonomic information if available
  • e.g. only assigned to Class level, scientificName = Teleostei, scientificNameID = urn:lsid:marinespecies.org:taxname:293496

• For Biota incertea sedis, scientificNameID should be populated with urn:lsid:marinespecies.org:taxname:12.
• Recommend that you populate verbatimIdentification with the name that was originally documented (e.g. phototrophic eukaryote).

Sequences without a WoRMS ID (AphiaID)

• WoRMS is an extensive database of linnean names of marine species, confirmed by experts in each taxonomic group
• Within eDNA datasets there are usually many taxonomic assignments (protists, prokaryotes) that cannot be found in the WoRMS database currently (i.e. no AphiaID available)
• The name might be available and mapped to GBIF, making sharing datasets between GBIF and OBIS complicated
• The submitter of the data may in these cases record the known species name, in scientificName, the ID of the taxon in another database in taxonConceptID and leave scientificNameID empty

Control samples

• Control samples are important for quality control of resulting data
• Currently, there is no feature for this implemented in the IPT
• In the future, OBIS and GBIF are working on a solution to submit them as separate files within the same dataset
• They will then not be mapped, to avoid that they end up in the GBIF or OBIS occurrence indexes, but will be available for anyone looking for that information

Metadata with no DwC terms

• A lot of information, especially detailed parameters e.g. of bioinformatic workflow, do not have controlled vocabulary fields yet
• The FAIRe project, “making eDNA data FAIR”, is in the process of developing new metadata fields to record all the relevant information of eDNA projects
• The checklist is already available online: https://fair-edna.github.io/download.html
• A lot of existing fields, a lot of new fields
• Currently, the new fields are dropped from the IPT. But in the process of adding them to DwC and MIxS.

Resources for DNA terms

Resource	Link
DNA Extension term definitions	rs.gbif.org/extension/gbif/1.0/dna_derived_data_2024-07-11.xml
DwC Term Quick Reference Guide	dwc.tdwg.org/terms/
OBIS Manual (DNA chapter)	manual.obis.org/dna_data.html
GBIF DNA publishing guide	doi.org/10.35035/DOC-VF1A-NR22